Assessing greenhouse gas emissions in Cuban agricultural soils: Implications for climate change and rice (Oryza sativa L.) production.

Assessing the impact of greenhouse gas (GHG) emissions on agricultural soils is crucial for ensuring food production sustainability in the global effort to combat climate change. The present study delves to comprehensively assess GHG emissions in Cuba's agricultural soil and analyze its implications for rice production and climate change because of its rich agriculture cultivation tradition and diverse agro-ecological zones from the period of 1990-2022. In this research, based on Autoregressive Distributed Lag (ARDL) approach the empirical findings depicts that in short run, a positive and significant impact of 1.60 percent % in Cuba's rice production. The higher amount of atmospheric carbon dioxide (CO2) levels improves photosynthesis, and stimulates the growth of rice plants, resulting in greater grain yields. On the other hand, rice production index raising GHG emissions from agriculture by 0.35 % in the short run. Furthermore, a significant and positive impact on rice production is found in relation to the farm machinery i.e., 3.1 %. Conversely, an adverse and significant impact of land quality was observed on rice production i.e., -5.5 %. The reliability of models was confirmed by CUSUM and CUSUM square plot. Diagnostic tests ensure the absence of serial correlation and heteroscedasticity in the models. Additionally, the forecasting results are obtained from the three machine learning models i.e. feed forward neural network (FFNN), support vector machines (SVM) and adaptive boosting technique (Adaboost). Through the % MAPE criterion, it is evident that FFNN has achieved high precision (91 %). Based on the empirical findings, the study proposed the adoption of sustainable agricultural practices and incentives should be given to the farmers so that future generations inherit a world that is sustainable, and healthy.

Long-term exposure to ambient fine particulate matter and carotid intima media thickness at bilateral, left and right in adults from Mexico City: Results from GEA study.

BACKGROUND: PM2.5 exposure has been associated with intima-media thickness (cIMT) increase. However, very few studies distinguished between left and right cIMT in relation to PM2.5 exposure. AIM: To evaluate associations between chronic exposure to PM2.5 and cIMT at bilateral, left, and right in adults from Mexico City. METHODS: This study comprised 913 participants from the control group, participants without personal or family history of cardiovascular disease, of the Genetics of Atherosclerosis Disease Mexican study (GEA acronym in Spanish), recruited at the Instituto Nacional de Cardiología Ignacio Chávez from June 2008 to January 2013. To assess the associations between chronic exposure to PM2.5 (per 5 µg/m3 increase) at different lag years (1-4 years) and cIMT (bilateral, left, and right) we applied distributed lag non-linear models (DLNMs). RESULTS: The median and interquartile range for cIMT at bilateral, left, and right, were 630 (555, 735), 640 (550, 750), and 620 (530, 720) µm, respectively. Annual average PM2.5 exposure was 26.64 µg/m3, with median and IQR, of 24.46 (23.5-25.46) µg/m3. Results from DLNMs adjusted for age, sex, body mass index, low-density lipoproteins, and glucose, showed that PM2.5 exposure for year 1 and 2, were positively and significantly associated with right-cIMT [6.99% (95% CI: 3.67; 10.42) and 2.98% (0.03; 6.01), respectively]. Negative associations were observed for PM2.5 at year 3 and 4 and right-cIMT; however only year 3 was statistically significant [-2.83% (95% CI: 5.12; -0.50)]. Left-cIMT was not associated with PM2.5 exposure at any lag year. The increase in bilateral cIMT followed a similar pattern as that observed for right-cIMT, but with lower estimates. CONCLUSIONS: Our results suggest different susceptibility between left and right cIMT associated with PM2.5 exposure highlighting the need of measuring both, left and right cIMT, regarding ambient air pollution in epidemiological studies.

Risk assessment of temperature and air pollutants on hospitalizations for mental and behavioral disorders in Curitiba, Brazil.

BACKGROUND: Extreme ambient temperatures and air quality have been directly associated with various human diseases from several studies around the world. However, few analyses involving the association of these environmental circumstances with mental and behavioral disorders (MBD) have been carried out, especially in developing countries such as Brazil. METHODS: A time series study was carried out to explore the associations between daily air pollutants (SO2, NO2, O3, and PM10) concentrations and meteorological variables (temperature and relative humidity) on hospital admissions for mental and behavioral disorders for Curitiba, Brazil. Daily hospital admissions from 2010 to 2016 were analyzed by a semi-parametric generalized additive model (GAM) combined with a distributed lag non-linear model (DLNM). RESULTS: Significant associations between environmental conditions (10 µg/m3 increase in air pollutants and temperature °C) and hospitalizations by MBD were found. Air temperature was the environmental variable with the highest relative risk (RR) at 0-day lag for all ages and sexes analyzed, with RR values of 1.0182 (95% CI: 1.0009-1.0357) for men, and 1.0407 (95% CI: 1.0230-1.0587) for women. Ozone exposure was a risk for all women groups, being higher for the young group, with a RR of 1.0319 (95% CI: 1.0165-1.0483). Elderly from both sexes were more susceptible to temperature variability, with a RR of 1.0651 (95% CI: 1.0213-1.1117) for women, and 1.0215 (95% CI: 1.0195-1.0716) for men. CONCLUSIONS: This study suggests that temperatures above and below the thermal comfort threshold, in addition to high concentrations of air pollutants, present significant risks on hospitalizations by MBD; besides, there are physiological and age differences resulting from the effect of this exposure.

Identifying critical windows of prenatal particulate matter (PM2.5) exposure and early childhood blood pressure.

BACKGROUND: Exposure to air pollution is associated with increased blood pressure (BP) in adults and children. Some evidence suggests that air pollution exposure during the prenatal period may contribute to adverse cardiorenal health later in life. Here we apply a distributed lag model (DLM) approach to identify critical windows that may underlie the association between prenatal particulate matter ≤ 2.5 µm in diameter (PM2.5) exposure and children's BP at ages 4-6 years. METHODS: Participants included 537 mother-child dyads enrolled in the Programming Research in Obesity, GRowth Environment, and Social Stress (PROGRESS) longitudinal birth cohort study based in Mexico City. Prenatal daily PM2.5 exposure was estimated using a validated satellite-based spatio-temporal model and BP was measured using the automated Spacelabs system with a sized cuff. We used distributed lag models (DLMs) to examine associations between daily PM2.5 exposure and systolic and diastolic BP (SBP and DBP), adjusting for child's age, sex and BMI, as well as maternal education, preeclampsia and indoor smoking report during the second and third trimester, seasonality and average postnatal year 1 PM2.5 exposure. RESULTS: We found that PM2.5 exposure between weeks 11-32 of gestation (days 80-226) was significantly associated with children's increased SBP. Similarly, PM2.5 exposure between weeks 9-25 of gestation (days 63-176) was significantly associated with increased DBP. To place this into context, a constant 10 µg/m3 increase in PM2.5 sustained throughout this critical window would predict a cumulative increase of 2.6 mmHg (CI: 0.5, 4.6) in SBP and 0.88 mmHg (CI: 0.1, 1.6) in DBP at ages 4-6 years. In a stratified analysis by sex, this association persisted in boys but not in girls. CONCLUSIONS: Second and third trimester PM2.5 exposure may increase children's BP in early life. Further work investigating PM2.5 exposure with BP trajectories later in childhood will be important to understanding cardiorenal trajectories that may predict adult disease. Our results underscore the importance of reducing air pollution exposure among susceptible populations, including pregnant women.

Association between prenatal particulate air pollution exposure and telomere length in cord blood: Effect modification by fetal sex.

INTRODUCTION: In utero particulate matter exposure produces oxidative stress that impacts cellular processes that include telomere biology. Newborn telomere length is likely critical to an individual's telomere biology; reduction in this initial telomere setting may signal increased susceptibility to adverse outcomes later in life. We examined associations between prenatal particulate matter with diameter ≤2.5 µm (PM2.5) and relative leukocyte telomere length (LTL) measured in cord blood using a data-driven approach to characterize sensitive windows of prenatal PM2.5 effects and explore sex differences. METHODS: Women who were residents of Mexico City and affiliated with the Mexican Social Security System were recruited during pregnancy (n = 423 for analyses). Mothers' prenatal exposure to PM2.5 was estimated based on residence during pregnancy using a validated satellite-based spatio-temporally resolved prediction model. Leukocyte DNA was extracted from cord blood obtained at delivery. Duplex quantitative polymerase chain reaction was used to compare the relative amplification of the telomere repeat copy number to single gene (albumin) copy number. A distributed lag model incorporating weekly averages for PM2.5 over gestation was used in order to explore sensitive windows. Sex-specific associations were examined using Bayesian distributed lag interaction models. RESULTS: In models that included child's sex, mother's age at delivery, prenatal environmental tobacco smoke exposure, pre-pregnancy BMI, gestational age, birth season and assay batch, we found significant associations between higher PM2.5 exposure during early pregnancy (4-9 weeks) and shorter LTL in cord blood. We also identified two more windows at 14-19 and 34-36 weeks in which increased PM2.5 exposure was associated with longer LTL. In stratified analyses, the mean and cumulative associations between PM2.5 and shortened LTL were stronger in girls when compared to boys. CONCLUSIONS: Increased PM2.5 during specific prenatal windows was associated with shorter LTL and longer LTL. PM2.5 was more strongly associated with shortened LTL in girls when compared to boys. Understanding sex and temporal differences in response to air pollution may provide unique insight into mechanisms.

Modelling malaria incidence by an autoregressive distributed lag model with spatial component.

The influence of climatic variables on the dynamics of human malaria has been widely highlighted. Also, it is known that this mosquito-borne infection varies in space and time. However, when the data is spatially incomplete most popular spatio-temporal methods of analysis cannot be applied directly. In this paper, we develop a two step methodology to model the spatio-temporal dependence of malaria incidence on local rainfall, temperature, and humidity as well as the regional sea surface temperatures (SST) in the northern coast of Venezuela. First, we fit an autoregressive distributed lag model (ARDL) to the weekly data, and then, we adjust a linear separable spacial vectorial autoregressive model (VAR) to the residuals of the ARDL. Finally, the model parameters are tuned using a Markov Chain Monte Carlo (MCMC) procedure derived from the Metropolis-Hastings algorithm. Our results show that the best model to account for the variations of malaria incidence from 2001 to 2008 in 10 endemic Municipalities in North-Eastern Venezuela is a logit model that included the accumulated local precipitation in combination with the local maximum temperature of the preceding month as positive regressors. Additionally, we show that although malaria dynamics is highly heterogeneous in space, a detailed analysis of the estimated spatial parameters in our model yield important insights regarding the joint behavior of the disease incidence across the different counties in our study.